title: Cell nuclei segmentation in fluorescence microscopy images using inter- and intra-region discriminative information.
abstract: Automated segmentation of cell nuclei in microscopic images is critical to high throughput analysis of the ever increasing amount of data. Although cell nuclei are generally visually distinguishable for human, automated segmentation faces challenges when there is significant intensity inhomogeneity among cell nuclei or in the background. In this paper, we propose an effective method for automated cell nucleus segmentation using a three-step approach. It first obtains an initial segmentation by extracting salient regions in the image, then reduces false positives using inter-region feature discrimination, and finally refines the boundary of the cell nuclei using intra-region contrast information. This method has been evaluated on two publicly available datasets of fluorescence microscopic images with 4009 cells, and has achieved superior performance compared to popular state of the art methods using established metrics.
authors.forename: Yang
authors.initials: Y
authors.lastname: Song
authors.forename: Weidong
authors.initials: W
authors.lastname: Cai
authors.forename: David Dagan
authors.initials: DD
authors.lastname: Feng
authors.forename: Mei
authors.initials: M
authors.lastname: Chen
mesh_terms: D000818:Animals; D002467:Cell Nucleus / Q000648:ultrastructure*; D003287:Contrast Media; D007091:Image Processing, Computer-Assisted / Q000379:methods; D051379:Mice; D008856:Microscopy, Fluorescence / Q000379:methods*; D041681:NIH 3T3 Cells
chemical_list: D003287:Contrast Media